In the field of medical imaging, various systems have been developed for generating medical images of various anatomical structures of individuals for the purpose of screening and evaluating medical conditions. These imaging systems include, for example, CT (computed tomography) imaging, MRI (magnetic resonance imaging), X-ray systems, ultrasound systems, PET (positron emission tomography) systems, etc. Each imaging modality may provide unique advantages over other modalities for screening and evaluating certain types of diseases, medical conditions or anatomical abnormalities, including, for example, colonic polyps, aneurisms, lung nodules, calcification on heart or artery tissue, cancer micro calcifications or masses in breast tissue, and various other lesions or abnormalities.
For example, as is well known in the art, CT (computed tomography) imaging systems can be used to obtain a set of cross-sectional images or 2D “slices” of a ROI (region-of-interest) of a patient for purposes of imaging organs and other anatomies. The CT imaging modality is commonly employed for purposes of diagnosing disease because such modality provides a more precise image that illustrates the size, shape, and location of various anatomical structures such as organs, soft tissues, and bones, and also enables a more accurate evaluation of lesions and abnormal anatomical structures such as cancer, polyps, etc.
One conventional method that physicians, clinicians, radiologists, etc, use for detecting, diagnosing or otherwise evaluating medical conditions is to manually review hard-copies (X-ray films, prints, photographs, etc) of medical images that are reconstructed from an acquired image dataset, to discern characteristic features of interest. For example, CT image data that is acquired during a CT examination can be used to produce a set of 2D medical images (X-ray films) that can be viewed to identify potential abnormal anatomical structures or lesions, for example, based upon the skill and knowledge of the reviewing physician, clinician, radiologist, etc. For example, a mammogram procedure may produce a set of medical images that include normal anatomical structures corresponding to breast tissue, but a trained radiologist may be able identify small lesions among these structures that are potentially cancerous. However, human error may cause a trained radiologist, physician, clinician, etc., to misdiagnose a medical condition such as breast cancer when manually reviewing such medical images.
Accordingly, various data processing systems and tools have been developed to assist physicians, clinicians, radiologists, etc, in evaluating medical images to identify and/or diagnose medical conditions. For example, CAD support systems and tools have been developed for various clinical applications for processing medical images and providing automated detection, evaluation, diagnosis, etc. of medical conditions using medical images. More specifically, by way of example, conventional CAD systems employ image data processing methods to automatically detect and diagnose potential medical conditions including, for example, lesions and other abnormal anatomical structures such as colonic polyps, aneurisms, lung nodules, calcification on heart or artery tissue, micro calcifications or masses in breast tissue, disease states, etc. When the processed image data is rendered and displayed, the CAD detected regions of interest in the displayed image are “marked” or otherwise highlighted to direct the attention of the user (e.g., physician, radiologist, etc.) to assess such CAD detected regions of interest.
Moreover, conventional CAD systems enable a user to invoke an interpretation tool while reviewing a particular (selected) CAD mark to process the image data and obtain results that can aid the user in evaluating or diagnosing the marked region. With such conventional CAD tools, review of CAD marks requires the user to separately invoke one interpretation tool at a given time for each CAD mark. Such process can be time consuming and user-unfriendly when, for instance, a relatively large amount of marked regions must be reviewed and the tools individually invoked.